The Impact of Exchange Rate and Oil Price Fluctuations on Iran's Agriculture Trade Balance: Application of the J Curve Approach

مجله اقتصاد و توسعه کشاورزی

Article 5, Volume 34, Issue 2 - Serial Number 48, July 2020, Pages 179-200   

Document Type: Research Article

DOI: 10.22067/jead2.v34i2.85637

Authors

S. Esmaili¹; M. Ghahremanzadeh^{* 2}; A. Mahmoudi¹; M. Mehrara³; Gh.R. Yavari¹

¹Payam Noor University, Tehran, Iran

²Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

³Faculty of Economics, Tehran University, Tehran, Iran

Abstract

Introduction: Exchange rate and oil prices are the important factors for foreign trade in any country and even fluctuation in these variables will affect the economic and trade growth. The purpose of this study is to investigate the effect of exchange rate and oil price fluctuations on trade balance of Iran's agriculture sector with its 8 major trading partner over the period 1998 to 2017 and examine also the existence of the J Curve in these countries. To this end, linear and nonlinear ARDL models were utilized based on literature and tried to determine lung-run and short run effect of underling variables. Then, the results of linear and non-linear ARDL models were compared.
Materials and Methods Methodology: Since eight countries, including the United Arab Emirates (UAE), Iraq, Afghanistan, Turkey, Korea, India, Germany and China are Iran's largest trading partners during 1998-2017, we focused on these countries. In this context, the model proposed by Oskoee et al. (2011) has been used to evaluate the impact of exchange rate and oil price fluctuations on agriculture trade balance. To capture the exchange rate and oil price fluctuations, the GARCH family models were applied (including EGARCHT, GARCH, SAGARCH, and NGARCH). Time series of exchange rate and oil price fluctuations which are extracted from GARCH models, are expected to be stationary. So, according to the empirical studies, the ARDL model is an appropriate model. However, both linear and nonlinear ARDL models were estimated. To specify trade balance equation, variables including Iran's GDP, GDP of eight trading partner countries, exchange rate, Oil prices fluctuations, exchange rate fluctuations and economic sanctions have been used. We used the ADF unit root test to check stationary of the variables.
Results and Discussion: The estimated results of the GARCH family models show that the sum of the coefficients of α+β for Turkey, Iraq, India, China, Afghanistan, Germany, and Korea are 0.88, 0.94, 1, 0.92, and 0.82, respectively. As the sum of the coefficients must be between 0 and 1, the predicted fluctuations series of exchange rate are stationary and also the predicted fluctuations series is as well. After obtaining fluctuations series of exchange rate and oil price, the number of optimal lags should be determined in ARDL model. According to the FPE criterion, the optimal lag is two and according to the AIC and SBC the optimal one is three.  Since the number of observations is low, the optimal lag number was selected two and the Linear and non-linear ARDL model was estimated. The results revealed that if Iran's GDP increased by 1%, the trade balance between Iran and Turkey would  improve by 18.20% and this value for Iraq, Afghanistan, UAE, China, Germany, Korea would be 59.07, 8.40, 26.28, 91.17, 16.32, 0.16, 22.02 respectively. In the long run, if Turkey's GDP rises 1%, the trade balance between Iran and Turkey will improve 14.34%. Moreover, if GDP in Iraq, Afghanistan, UAE, Chinese, German, Korean climb by 1%, the trade balance reaches 38.31, 7.003, 10.41, 17.99, 0.39 24.6 respectively. If the exchange rate rises 1% in Iraq and Germany, the trade balance will improve roughly 26.9, 69.4 respectively. Escalating National currency in Turkey and India has reverse effects on the trade balance. In fact, as the exchange rate rises, imports from Turkey and India increased and this contradiction may be due to sanctions and economic conditions. In China and India, positive and negative fluctuation has positive and negative effects on the trade balance. Indeed, by increasing the positive exchange rate fluctuations, the trade balance would improve and with the negative exchange rate fluctuations (the exchange rate decline) the trade balance might worsen. In the nonlinear ARDL method, exchange rate fluctuations in India and China are positive and have significant effect, and it shows that there is a j-curve between Iran and these countries. Also separating exchange rate fluctuations in positive and negative groups can prove the existence of the j Curve.
Conclusion: According to the results, the highest value of agricultural exports is related to Iraq and the least is to Korea. The UAE has the highest imports from Iran and Iraq has the lowest one. The co-integration test reveals that the underlining variables follow and influence each other in the long run. Based on previous studies and predicted signs for coefficients of the variables in the models, the non-linear ARDL model provides better results. The finding showed that GDP of 8 countries were positive and had significant effects and Iran's GDP was negative and significant in these eight countries. In the long run, an oil price fluctuation in Turkey, Afghanistan, Germany and India has positive and significant impact. In fact, as oil prices increase, the agricultural trade balance improves. In the short run, as oil prices rise, the agricultural trade balance would decline in countries such as Turkey, Germany and India and increase in Iraq and China. By dividing the exchange rate fluctuations into positive and negative parts, we conclude that positive exchange rate fluctuations in China and India have a positive effect on the trade balance and negative fluctuations have a negative effect on the trade Balance. The current study confirmed the existence of the J curve in India and China.

Keywords

Exchange rate fluctuations; Linear ARDL; Nonlinear ARDL; Oil price fluctuations; J curve

References

1. Ansari V., Salami H., and Saleh A. 2010. Growth Resources in the Iranian Agriculture Sector: An Analysis in the Output-Output Table. Iranian. Journal of Agricultural Economics and Development Research 42-2(1): 1-17. (In Persian)
2. Bazazan F., Shirin Bakhsh Masouleh SH., and Asfari S. 2013. The Effects of Weekdays on Stock Returns: The Garch and Weststrap Regression Approach. Empirical Accounting Researc 3(16): 149-160. (In Persian)
3. Bakhshi P., Rahli H., and Ghahramanzadeh M. 2016. The Impact of Oil Revenue Shocks and Exchange Rate Uncertainty on Agricultural Growth in Iran. Agricultural Economics Research 3(31): 101-122. (In Persian)
4. Bahmani-Oskooee M., Harvey H., and Hegerty S.W. 2012. Exchange-rate volatility and Industry Trade between the S and KOREA. Journal of Economic Development 37(1): 1-27.
5. Bahmani-Oskooee M., and Fariditavana H. 2015. Asymmetric cointegration and the J-curve: new Evidence from Commodity Trade between the U.S. and Canada. International Economics and Economic Policy: 1-56.
6. Berument H., Ceylan N., and Dogan N. 2010. The Impact of Oil Price Shocks on the Economic Growth of Selected MENA1 Countries. The Energy Journal 31(1): 149-176.
7. Chaudhary Gh.M., Hashmib SH., and Asif Khan M. 2016. Exchange Rate and Foreign Trade: A Comparative Study of Major South Asian and South-East Asian Countries Procedia - Social and Behavioral Sciences 230: 85-93.
8. Esmaeil F., and Mazraeh F. 2016. Economic Analysis of Short-term and Long-term Effects of Exchange Rate Uncertainty on the Export of Iranian Saffron. Saffron Journal of Agriculture and Technology 6(3):367-381. (In Persian)
9. Emami , and Maleki E. 2014. The Effect of Exchange Rate Fluctuations on Employment in Iran ", Journal of Economic Science 26: 95-112. (In Persian)
10. Engle RF., and Granger CWJ. 1987. Cointegration and Error Correction: Representation, Estimation, and Testing. Econometrica 55(2): 251–276.
11. Falahi E., and Mazraie F. 2017. Economic Analysis of Short and Long Term Impacts of Exchange Rate Uncertainty on Iranian Saffron Exports. Saffron Journal of Agriculture and Technology 6(3): 367-38. (In Persian)
12. Kazeroni E., Rezazadeh E., and Mohammadpor S. 2010. "Asymetric Effects Real Exchange Rate Instability on Nonoil Export: Nonlinear Markov Sweeching Aproach", Journal of Modeling Reaserch 5: 153-178. (In Persian)
13. Ling Wang S., and McPhail L. 2014. Impacts of Energy Shocks on US Agricultural Productivity Growth and Commodity Prices a Structural VAR Analysis. Economic Research Service 46: 435-444
14. Magee S. 1973. Currency Contracts, Pass Through and Devaluation. BrookingPapers on Economic Activity 1: 303–325.
15. Mehrabiyan M., Abdi A.R. 2007. Factors Determining Trade Balance in Iranian Economy. Iranian Journal of Economic Research.9(31):1-26
16. Mohamadi H., Mohammadi M., and Sakhi F. 2018. Investigating the Effect of Real Exchange Rate Uncertainty on Foreign Trade of Iranian Agricultural Products. Agricultural Economics Research 1(37): 20-41. (In Persian)
17. Nematolahi F., and Tabatabayi Sh. 2011. The Impact of OPEC Oil Price Fluctuations on Iran's Trade Balance. Journal of Economic Modeling 3(4)151-169. (In Persian)
18. Pesaran M.H., and Shin Y. 1996. Cointegration and speed of convergence to equilibrium. Journal of Econometrics 71(1-2): 117-143.
19. Pishbahr S., Rahimi J., Dashti G., and Gahramanzade M. 2015. The Effects of Agricultural Iinstability and Trade Fluctuations on Agricultural Sector Growth in Iran. Iranian Journal of Agricultural Economics and Development Research 46(2): 299-310. (In Persian)
20. Pick D.H., and Vollrath L. 1994. Real Exchange Rate Misalignment and Agricultural Export Performance in Diveloping Countries, Economic Development and Cultural 42(1): 554- 571.
21. Paris A. 2018. On the link between oil and Agricultural Commodity prices: Do Biofuels Matter?," International Economics, Elsevier 155(C): 48-60.
22. Ramezani A. 2016. The effect of factors on bilateral trade balance between Iran and India. First National Conference on New Approaches to Accounting and Management.
23. Shokri A., Shahnoshi N., Mohammadzadeh R., and Azarinfar Y. 2003. Factors Affecting Investment in Iranian Agriculture Sector. Journal of Agricultural Economics Research 1(2): 107-121.
24. Shin Y., Yu B., and Greenwood-Nimmo M. 2013. Modelling Asymmetric. Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework Festschrift, forthcoming, pringer. Festschrift in Honor of Peter Schmidt 281-314.
25. Samadi S., Yahya Abadi A., and Moalemi N. 2010. Analysis of the effect of oil price shocks on macroeconomic variables in Iran. Journal of Research and Economic Policies 52: 5-26.
26. Yazdani M., and Jangi R. 2017. Real exchange rate and consistent s & j curve Between Iran and top trading partners. Journal of Economics and Modeling Shahid Beheshti University 7(28): 31-53.
27. Yazdani M., and Norafza T. 2015. Assessing the Effect of Oil Price Fluctuations and Production Gap on Trade Balance of Iranian Economy. Journal of Applied Economics 9(31): 22-40.
28. Zhang Ch., Liu F., and Yu D. 2018. Dynamic Jumps in Global Oil Price and its Impacts on China's Bulk Commodities. Energy Economics 70: 297–306.